{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Action1_SupplyChain_回归预测\n",
    "\n",
    "数据集：SupplyChainDataset.csv，供应链采购数据     To Do：      \n",
    "\n",
    "对于销售额进行预测，即Sales字段     \n",
    "\n",
    "对于订货数量进行预测，即Order Item Quantity\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 回归预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据预处理后的data持久化\n",
    "# import pickle\n",
    "# with open('data.pkl', 'wb') as file:\n",
    "#     pickle.dump(data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th>Customer Full Name</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_week_day</th>\n",
       "      <th>order_hour</th>\n",
       "      <th>order_month_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>91.250000</td>\n",
       "      <td>314.640015</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>2/3/2018 22:56</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CallyHolloway</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-249.089996</td>\n",
       "      <td>311.359985</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2018 12:27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>IreneLuna</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GillianMaldonado</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/16/2018 11:45</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TanaTate</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>134.210007</td>\n",
       "      <td>298.250000</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 11:24</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>OrliHendricks</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>...</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 3:40</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MariaPeterson</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>RonaldClark</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>141.110001</td>\n",
       "      <td>391.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>...</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 21:00</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JohnSmith</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2016-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>186.229996</td>\n",
       "      <td>387.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 20:18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MarySmith</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>168.949997</td>\n",
       "      <td>383.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 18:54</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AndreaOrtega</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>2016-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0          DEBIT                         3                              4   \n",
       "1       TRANSFER                         5                              4   \n",
       "2           CASH                         4                              4   \n",
       "3          DEBIT                         3                              4   \n",
       "4        PAYMENT                         2                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180514      CASH                         4                              4   \n",
       "180515     DEBIT                         3                              2   \n",
       "180516  TRANSFER                         5                              4   \n",
       "180517   PAYMENT                         3                              4   \n",
       "180518   PAYMENT                         4                              4   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "0               91.250000          314.640015  Advance shipping   \n",
       "1             -249.089996          311.359985     Late delivery   \n",
       "2             -247.779999          309.720001  Shipping on time   \n",
       "3               22.860001          304.809998  Advance shipping   \n",
       "4              134.210007          298.250000  Advance shipping   \n",
       "...                   ...                 ...               ...   \n",
       "180514          40.000000          399.980011  Shipping on time   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "180516         141.110001          391.980011     Late delivery   \n",
       "180517         186.229996          387.980011  Advance shipping   \n",
       "180518         168.949997          383.980011  Shipping on time   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                        0           73  Sporting Goods        Caguas  ...   \n",
       "1                        1           73  Sporting Goods        Caguas  ...   \n",
       "2                        0           73  Sporting Goods      San Jose  ...   \n",
       "3                        0           73  Sporting Goods   Los Angeles  ...   \n",
       "4                        0           73  Sporting Goods        Caguas  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180514                   0           45         Fishing      Brooklyn  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "180516                   1           45         Fishing       Bristol  ...   \n",
       "180517                   0           45         Fishing        Caguas  ...   \n",
       "180518                   0           45         Fishing        Caguas  ...   \n",
       "\n",
       "       Product Price Product Status shipping date (DateOrders)  \\\n",
       "0         327.750000              0             2/3/2018 22:56   \n",
       "1         327.750000              0            1/18/2018 12:27   \n",
       "2         327.750000              0            1/17/2018 12:06   \n",
       "3         327.750000              0            1/16/2018 11:45   \n",
       "4         327.750000              0            1/15/2018 11:24   \n",
       "...              ...            ...                        ...   \n",
       "180514    399.980011              0             1/20/2016 3:40   \n",
       "180515    399.980011              0             1/19/2016 1:34   \n",
       "180516    399.980011              0            1/20/2016 21:00   \n",
       "180517    399.980011              0            1/18/2016 20:18   \n",
       "180518    399.980011              0            1/19/2016 18:54   \n",
       "\n",
       "         Shipping Mode Customer Full Name order_year order_month  \\\n",
       "0       Standard Class      CallyHolloway       2018           1   \n",
       "1       Standard Class          IreneLuna       2018           1   \n",
       "2       Standard Class   GillianMaldonado       2018           1   \n",
       "3       Standard Class           TanaTate       2018           1   \n",
       "4       Standard Class      OrliHendricks       2018           1   \n",
       "...                ...                ...        ...         ...   \n",
       "180514  Standard Class      MariaPeterson       2016           1   \n",
       "180515    Second Class        RonaldClark       2016           1   \n",
       "180516  Standard Class          JohnSmith       2016           1   \n",
       "180517  Standard Class          MarySmith       2016           1   \n",
       "180518  Standard Class       AndreaOrtega       2016           1   \n",
       "\n",
       "       order_week_day order_hour  order_month_year  \n",
       "0                   2         22           2018-01  \n",
       "1                   5         12           2018-01  \n",
       "2                   5         12           2018-01  \n",
       "3                   5         11           2018-01  \n",
       "4                   5         11           2018-01  \n",
       "...               ...        ...               ...  \n",
       "180514              5          3           2016-01  \n",
       "180515              5          1           2016-01  \n",
       "180516              4         21           2016-01  \n",
       "180517              4         20           2016-01  \n",
       "180518              4         18           2016-01  \n",
       "\n",
       "[180519 rows x 59 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "with open('data.pkl', 'rb') as file:\n",
    "    train_data = pickle.load(file)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180511, 34)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop(['Customer Email','Customer Password','Product Description','Product Status','Product Image','Customer Lname','Customer Fname'],axis=1,inplace=True) # drop掉列\n",
    "\n",
    "\n",
    "# 把不相关的内容去掉,约定去掉一个轴方向即可\n",
    "train_data.drop(['Order Customer Id','Order Item Cardprod Id','Order Item Id','Sales per customer','Order Item Total','Order Profit Per Order','Product Card Id','Product Category Id','Product Price'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "train_data.drop(['Order Zipcode','shipping date (DateOrders)','Latitude','Longitude','Customer Street'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "# 时间暂不处理drop掉\n",
    "train_data.drop(['order date (DateOrders)'],axis=1,inplace=True)\n",
    "train_data.drop(['order_month_year'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "# 标签泄露处理\n",
    "train_data.drop(['Order Status','Delivery Status'], axis=1, inplace=True)\n",
    "train_data = train_data.dropna(subset=['Customer Full Name']) # 如果有缺失行则drop掉\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Type', 'Category Name', 'Customer City', 'Customer Country',\n",
       "       'Customer Segment', 'Customer State', 'Department Name', 'Market',\n",
       "       'Order City', 'Order Country', 'Order Region', 'Order State',\n",
       "       'Product Name', 'Shipping Mode', 'Customer Full Name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看分类类型\n",
    "categorical_cols = train_data.select_dtypes(include='object').columns\n",
    "categorical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f749d4954504>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_data[cat] = le.fit_transform(train_data[cat])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>Customer Country</th>\n",
       "      <th>Customer Segment</th>\n",
       "      <th>Customer State</th>\n",
       "      <th>Department Name</th>\n",
       "      <th>Market</th>\n",
       "      <th>Order City</th>\n",
       "      <th>Order Country</th>\n",
       "      <th>Order Region</th>\n",
       "      <th>Order State</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th>Customer Full Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>331</td>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "      <td>475</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>1875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>391</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>841</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>5374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>452</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>391</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>841</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>4426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>285</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3226</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>835</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>12922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3226</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>835</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>10632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2922</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>913</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>8421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1362</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>770</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>11672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>6296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>9379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2203</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>967</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180511 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Type  Category Name  Customer City  Customer Country  \\\n",
       "0          1             40             66                 1   \n",
       "1          3             40             66                 1   \n",
       "2          0             40            452                 0   \n",
       "3          1             40            285                 0   \n",
       "4          2             40             66                 1   \n",
       "...      ...            ...            ...               ...   \n",
       "180514     0             18             59                 0   \n",
       "180515     1             18             26                 0   \n",
       "180516     3             18             55                 0   \n",
       "180517     2             18             66                 1   \n",
       "180518     2             18             66                 1   \n",
       "\n",
       "        Customer Segment  Customer State  Department Name  Market  Order City  \\\n",
       "0                      0              36                4       3         331   \n",
       "1                      0              36                4       3         391   \n",
       "2                      0               5                4       3         391   \n",
       "3                      2               5                4       3        3226   \n",
       "4                      1              36                4       3        3226   \n",
       "...                  ...             ...              ...     ...         ...   \n",
       "180514                 2              31                3       3        2922   \n",
       "180515                 1               5                3       3        1362   \n",
       "180516                 1               7                3       3          25   \n",
       "180517                 0              36                3       3          25   \n",
       "180518                 0              36                3       3        2203   \n",
       "\n",
       "        Order Country  Order Region  Order State  Product Name  Shipping Mode  \\\n",
       "0                  70            15          475            78              3   \n",
       "1                  69            13          841            78              3   \n",
       "2                  69            13          841            78              3   \n",
       "3                   8            11          835            78              3   \n",
       "4                   8            11          835            78              3   \n",
       "...               ...           ...          ...           ...            ...   \n",
       "180514             31             7          913            24              3   \n",
       "180515             77             7          770            24              2   \n",
       "180516              8            11           88            24              3   \n",
       "180517              8            11           88            24              3   \n",
       "180518             69            13          967            24              3   \n",
       "\n",
       "        Customer Full Name  \n",
       "0                     1875  \n",
       "1                     5374  \n",
       "2                     4426  \n",
       "3                    12922  \n",
       "4                    10632  \n",
       "...                    ...  \n",
       "180514                8421  \n",
       "180515               11672  \n",
       "180516                6296  \n",
       "180517                9379  \n",
       "180518                 658  \n",
       "\n",
       "[180511 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# object类型labelencoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "for cat in categorical_cols:\n",
    "    train_data[cat] = le.fit_transform(train_data[cat])\n",
    "train_data[categorical_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Days for shipping (real)',\n",
       " 'Days for shipment (scheduled)',\n",
       " 'Benefit per order',\n",
       " 'Late_delivery_risk',\n",
       " 'Category Id',\n",
       " 'Customer Id',\n",
       " 'Customer Zipcode',\n",
       " 'Department Id',\n",
       " 'Order Id',\n",
       " 'Order Item Discount',\n",
       " 'Order Item Discount Rate',\n",
       " 'Order Item Product Price',\n",
       " 'Order Item Profit Ratio',\n",
       " 'Order Item Quantity',\n",
       " 'Sales',\n",
       " 'order_year',\n",
       " 'order_month',\n",
       " 'order_week_day',\n",
       " 'order_hour']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到所有数值类型\n",
    "numerical_cols = train_data.columns.tolist()\n",
    "for x in categorical_cols.tolist():\n",
    "    numerical_cols.remove(x)\n",
    "numerical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sales、quantity预测\n",
    "import numpy as np\n",
    "x_sales = train_data.loc[:, train_data.columns !='Sales']\n",
    "y_sales = train_data['Sales']\n",
    "\n",
    "x_quantity = train_data.loc[:, train_data.columns !='Order Item Quantity']\n",
    "y_quantity = train_data['Order Item Quantity']\n",
    "\n",
    "# 数据切分\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_sales_train, x_sales_test, y_sales_train, y_sales_test = train_test_split(x_sales, y_sales, test_size=0.2)\n",
    "x_quantity_train, x_quantity_test, y_quantity_train, y_quantity_test = train_test_split(x_quantity, y_quantity, test_size=0.2)\n",
    "\n",
    "\n",
    "# 回归模型采用mae、mse\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "\n",
    "def regression_model_stats(model, x_train, x_test, y_train, y_test, model_name='Sales'):\n",
    "    model = model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    print('{} MAE {}'.format(model_name,mae))\n",
    "    print('{} MSE {}'.format(model_name,mse))\n",
    "    return mae,mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salse MAE 18.344640736562525\n",
      "Salse MSE 948.8184011302435\n",
      "Quantity MAE 0.34784489448206884\n",
      "Quantity MSE 0.28090141001716146\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.34784489448206884, 0.28090141001716146)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model_sales = LinearRegression()\n",
    "model_quantity = LinearRegression()\n",
    "\n",
    "regression_model_stats(model_sales, x_sales_train, x_sales_test, y_sales_train, y_sales_test, model_name='Salse')\n",
    "regression_model_stats(model_quantity, x_quantity_train, x_quantity_test, y_quantity_train, y_quantity_test, model_name='Quantity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salse MAE 18.735670374019097\n",
      "Salse MSE 1043.2505689324869\n",
      "Quantity MAE 0.3636172743322913\n",
      "Quantity MSE 0.3031061321373935\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3636172743322913, 0.3031061321373935)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "model_sales = Lasso()\n",
    "model_quantity = Lasso()\n",
    "\n",
    "regression_model_stats(model_sales, x_sales_train, x_sales_test, y_sales_train, y_sales_test, model_name='Salse')\n",
    "regression_model_stats(model_quantity, x_quantity_train, x_quantity_test, y_quantity_train, y_quantity_test, model_name='Quantity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salse MAE 18.340429537239196\n",
      "Salse MSE 948.8533433975493\n",
      "Quantity MAE 0.3478412877579668\n",
      "Quantity MSE 0.28090137603091775\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3478412877579668, 0.28090137603091775)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "model_sales = Ridge()\n",
    "model_quantity = Ridge()\n",
    "\n",
    "regression_model_stats(model_sales, x_sales_train, x_sales_test, y_sales_train, y_sales_test, model_name='Salse')\n",
    "regression_model_stats(model_quantity, x_quantity_train, x_quantity_test, y_quantity_train, y_quantity_test, model_name='Quantity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salse MAE 0.019702915616425502\n",
      "Salse MSE 0.013666812145199434\n",
      "Quantity MAE 3.9548636889406856e-05\n",
      "Quantity MSE 2.415691754731919e-08\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3.9548636889406856e-05, 2.415691754731919e-08)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xgb\n",
    "import xgboost as xgb\n",
    "model_sales = xgb.XGBRegressor()\n",
    "model_quantity = xgb.XGBRegressor()\n",
    "\n",
    "regression_model_stats(model_sales, x_sales_train, x_sales_test, y_sales_train, y_sales_test, model_name='Salse')\n",
    "regression_model_stats(model_quantity, x_quantity_train, x_quantity_test, y_quantity_train, y_quantity_test, model_name='Quantity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(model_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(model_quantity)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit ('Bi_env': venv)",
   "language": "python",
   "name": "python38064bitbienvvenvba07af95a1bb4b078aa8134bba84dff2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
